Secondly, to capture temporal characteristics, we propose to use the positional encoding [1]} technique to inject time information into the above trajectory tokens. Positional encodings have been used in the self-attention model to capture information about token positions in a sequence. As a trajectory is an ordered sequence of tokens, we could use the same mechanism to model the sequence. We compute the positional encodings as Equation REF  and REF :
\(PE(t_{i}, 2p) = sin(\frac{t_{i}}{10000^{{2p}/{d_{cell}}}})\) 
\(PE(t_{i}, 2p + 1) = cos(\frac{t_{i}}{10000^{{2p}/{d_{cell}}}})\) 
